Filtering and smoothing state estimation for flag Hidden Markov Models | IEEE Conference Publication | IEEE Xplore

Filtering and smoothing state estimation for flag Hidden Markov Models


Abstract:

State detection is studied for a special class of flag Hidden Markov Models (HMMs), which comprise 1) an arbitrary finite-state underlying Markov chain and 2) a structure...Show More

Abstract:

State detection is studied for a special class of flag Hidden Markov Models (HMMs), which comprise 1) an arbitrary finite-state underlying Markov chain and 2) a structured observation process wherein a subset of states emit distinct flags while other states are unmeasured. For flag HMMs, an explicit computation of the probability of error for the maximum-likelihood smoother is developed. Also, some structural results are obtained for maximum likelihood detectors and their error probabilities. These algebraic and structural results are leveraged to address sensor placement in three examples, including one on activity-monitoring in a home environment that is drawn from field data.
Date of Conference: 06-08 July 2016
Date Added to IEEE Xplore: 01 August 2016
ISBN Information:
Electronic ISSN: 2378-5861
Conference Location: Boston, MA, USA

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